175 research outputs found
Convex Calibration Dimension for Multiclass Loss Matrices
We study consistency properties of surrogate loss functions for general
multiclass learning problems, defined by a general multiclass loss matrix. We
extend the notion of classification calibration, which has been studied for
binary and multiclass 0-1 classification problems (and for certain other
specific learning problems), to the general multiclass setting, and derive
necessary and sufficient conditions for a surrogate loss to be calibrated with
respect to a loss matrix in this setting. We then introduce the notion of
convex calibration dimension of a multiclass loss matrix, which measures the
smallest `size' of a prediction space in which it is possible to design a
convex surrogate that is calibrated with respect to the loss matrix. We derive
both upper and lower bounds on this quantity, and use these results to analyze
various loss matrices. In particular, we apply our framework to study various
subset ranking losses, and use the convex calibration dimension as a tool to
show both the existence and non-existence of various types of convex calibrated
surrogates for these losses. Our results strengthen recent results of Duchi et
al. (2010) and Calauzenes et al. (2012) on the non-existence of certain types
of convex calibrated surrogates in subset ranking. We anticipate the convex
calibration dimension may prove to be a useful tool in the study and design of
surrogate losses for general multiclass learning problems.Comment: Accepted to JMLR, pending editin
Transductive Ranking on Graphs
In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function in a transductive, graph-based setting, where the object space is finite and is represented as a graph in which vertices correspond to objects and edges encode similarities between objects. Building on recent developments in regularization theory for graphs and corresponding Laplacian-based learning methods, we develop an algorithmic framework for learning ranking functions on graphs. We derive generalization bounds for our algorithms in transductive models similar to those used to study other transductive learning problems, and give experimental evidence of the potential benefits of our framework
All slums are not equal: child health conditions among the urban poor
Increasing urbanization has resulted in a faster
growth of slum population. Various agencies,
especially those in developing countries are finding it
difficult to respond to this situation effectively.
Disparities among slums exist owing to various
factors. This has led to varying degrees of health
burden on the slum children. Child health conditions
in slums with inadequate services are worse in
comparison to relatively better served slums.
Identification, mapping and assessment of all slums is
important for locating the hitherto missed out slums
and focusing on the neediest slums. In view of the
differential vulnerabilities across slums, an urban
child health program should build context appropriate
and community-need-responsive approaches
to improve children’s health in the slums
On Consistent Surrogate Risk Minimization and Property Elicitation
Abstract Surrogate risk minimization is a popular framework for supervised learning; property elicitation is a widely studied area in probability forecasting, machine learning, statistics and economics. In this paper, we connect these two themes by showing that calibrated surrogate losses in supervised learning can essentially be viewed as eliciting or estimating certain properties of the underlying conditional label distribution that are sufficient to construct an optimal classifier under the target loss of interest. Our study helps to shed light on the design of convex calibrated surrogates. We also give a new framework for designing convex calibrated surrogates under low-noise conditions by eliciting properties that allow one to construct 'coarse' estimates of the underlying distribution
Impact of institutions on land cover change and landscape fragmentation in an Indian dry tropical forest landscapes
Protected Areas (PAs) have been a cornerstone of conservation efforts. However, PAs have become increasingly isolated with protection. Human pressure has shifted towards the forests located outside PAs, which serve as important corridors for wildlife movement. In densely populated countries like India, connectivity across vast landscapes is not possible solely by the
expansion of the PA network and requires support from local communities. The importance of local institutions has been considerably ignored due to the focus on PAs, which have limited capacity to meet local demands as well as conservation objectives for vast landscapes.
This Ph.D. research integrates remote sensing, landscape ecology and institutional approaches to study social and ecological impacts of forest management institutions in a dry-deciduous forest landscape in the Vidarbha region of Maharashtra, India. The study area forms an important
connection between Pench and Tadoba-Andhari Tiger Reserves. The study begins with a largescale landscape view to study the impact of different forest management regimes on forest change and fragmentation. It then zooms in to compare state and community institutions that
differ in traditional norms as well as levels of local participation, assessing their effect on forests and local communities
On Consistent Surrogate Risk Minimization and Property Elicitation
Abstract Surrogate risk minimization is a popular framework for supervised learning; property elicitation is a widely studied area in probability forecasting, machine learning, statistics and economics. In this paper, we connect these two themes by showing that calibrated surrogate losses in supervised learning can essentially be viewed as eliciting or estimating certain properties of the underlying conditional label distribution that are sufficient to construct an optimal classifier under the target loss of interest. Our study helps to shed light on the design of convex calibrated surrogates. We also give a new framework for designing convex calibrated surrogates under low-noise conditions by eliciting properties that allow one to construct 'coarse' estimates of the underlying distribution
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